Associative Forecasting

# Associative Forecasting - b = = = .25 ∑ xy - nxy ∑ x 2-...

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1 Associative Forecasting Used when changes in one or more independent variables can be used to predict the changes in the dependent variable Most common technique is linear regression analysis We apply this technique just as we did in the time series example

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2 Associative Forecasting Forecasting an outcome based on predictor variables using the least squares technique y = a + bx ^ where y = computed value of the variable to be predicted (dependent variable) a = y-axis intercept b = slope of the regression line x = the independent variable though to predict the value of the dependent variable ^
3 Associative Forecasting Example Sales Local Payroll (\$000,000), y (\$000,000,000), x 2.0 1 3.0 3 2.5 4 2.0 2 2.0 1 3.5 7 4.0 3.0 2.0 1.0 | | | | | | | 0 1 2 3 4 5 6 7 Sales Area payroll

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4 Associative Forecasting Example Sales, y Payroll, x x 2 xy 2.0 1 1 2.0 3.0 3 9 9.0 2.5 4 16 10.0 2.0 2 4 4.0 2.0 1 1 2.0 3.5 7 49 24.5 y = 15.0 x = 18 x 2 = 80 xy = 51.5 x = ∑ x /6 = 18/6 = 3 y = ∑ y /6 = 15/6 = 2.5

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Unformatted text preview: b = = = .25 ∑ xy - nxy ∑ x 2- nx 2 51.5 - (6)(3)(2.5) 80 - (6)(32) a = y- b x = 2.5 - (.25)(3) = 1.75 5 Associative Forecasting Example 4.0 – 3.0 – 2.0 – 1.0 – | | | | | | | 1 2 3 4 5 6 7 Sales Area payroll y = 1.75 + .25 x ^ Sales = 1.75 + .25( payroll ) If payroll next year is estimated to be \$600 million, then: Sales = 1.75 + .25(6) Sales = \$325,000 3.25 6 Focus Forecasting Developed at American Hardware Supply, focus forecasting is based on two principles: 1. Sophisticated forecasting models are not always better than simple models 2. There is no single techniques that should be used for all products or services This approach uses historical data to test multiple forecasting models for individual items The forecasting model with the lowest error is then used to forecast the next demand...
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## This note was uploaded on 01/20/2012 for the course MGT 3200 taught by Professor Moodie during the Spring '08 term at Kennesaw.

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Associative Forecasting - b = = = .25 ∑ xy - nxy ∑ x 2-...

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